Overview

Dataset statistics

Number of variables14
Number of observations44
Missing cells43
Missing cells (%)7.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.4 KiB
Average record size in memory125.0 B

Variable types

Numeric8
Categorical6

Dataset

Description2020.11~12에 제주도 내 44개 지역에서 측정한 감귤(노지감귤) 당도 측정 데이터
Author제주국제자유도시개발센터
URLhttps://www.data.go.kr/data/15077686/fileData.do

Alerts

경도 is highly overall correlated with 당도(brix)_2020년 12월 1주차 and 1 other fieldsHigh correlation
위도 is highly overall correlated with 지역(읍/면/동)High correlation
당도(brix)_2020년 11월 1주차 is highly overall correlated with 당도(brix)_2020년 11월 2주차 and 3 other fieldsHigh correlation
당도(brix)_2020년 11월 2주차 is highly overall correlated with 당도(brix)_2020년 11월 1주차 and 3 other fieldsHigh correlation
당도(brix)_2020년 11월 3주차 is highly overall correlated with 당도(brix)_2020년 11월 1주차 and 3 other fieldsHigh correlation
당도(brix)_2020년 11월 4주차 is highly overall correlated with 당도(brix)_2020년 11월 1주차 and 3 other fieldsHigh correlation
당도(brix)_2020년 12월 1주차 is highly overall correlated with 경도 and 4 other fieldsHigh correlation
지역(시) is highly overall correlated with 지역(읍/면/동)High correlation
지역(읍/면/동) is highly overall correlated with 경도 and 2 other fieldsHigh correlation
원지정비(O / X) is highly overall correlated with 나무간격(m)High correlation
나무간격(m) is highly overall correlated with 원지정비(O / X)High correlation
원지정비(O / X) is highly imbalanced (73.3%)Imbalance
당도(brix)_2020년 11월 3주차 has 3 (6.8%) missing valuesMissing
당도(brix)_2020년 11월 4주차 has 16 (36.4%) missing valuesMissing
당도(brix)_2020년 12월 1주차 has 24 (54.5%) missing valuesMissing
관리번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:18:22.993982
Analysis finished2023-12-12 20:18:31.721214
Duration8.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리번호
Real number (ℝ)

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.454545
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T05:18:32.154608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.15
Q115.75
median26.5
Q338.25
95-th percentile46.85
Maximum50
Range49
Interquartile range (IQR)22.5

Descriptive statistics

Standard deviation14.205231
Coefficient of variation (CV)0.53696749
Kurtosis-1.062197
Mean26.454545
Median Absolute Deviation (MAD)11.5
Skewness-0.13276356
Sum1164
Variance201.78858
MonotonicityStrictly increasing
2023-12-13T05:18:32.359988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 1
 
2.3%
28 1
 
2.3%
31 1
 
2.3%
32 1
 
2.3%
33 1
 
2.3%
34 1
 
2.3%
35 1
 
2.3%
36 1
 
2.3%
37 1
 
2.3%
38 1
 
2.3%
Other values (34) 34
77.3%
ValueCountFrequency (%)
1 1
2.3%
2 1
2.3%
3 1
2.3%
4 1
2.3%
5 1
2.3%
8 1
2.3%
10 1
2.3%
11 1
2.3%
13 1
2.3%
14 1
2.3%
ValueCountFrequency (%)
50 1
2.3%
49 1
2.3%
47 1
2.3%
46 1
2.3%
45 1
2.3%
44 1
2.3%
43 1
2.3%
42 1
2.3%
41 1
2.3%
40 1
2.3%

지역(시)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
서귀포시
35 
제주시
9 

Length

Max length4
Median length4
Mean length3.7954545
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서귀포시
2nd row서귀포시
3rd row서귀포시
4th row제주시
5th row제주시

Common Values

ValueCountFrequency (%)
서귀포시 35
79.5%
제주시 9
 
20.5%

Length

2023-12-13T05:18:32.539252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:32.701769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서귀포시 35
79.5%
제주시 9
 
20.5%

지역(읍/면/동)
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Memory size484.0 B
남원읍
14 
애월읍
5 
대포동
3 
조천읍
3 
서홍동
2 
Other values (13)
17 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique9 ?
Unique (%)20.5%

Sample

1st row대포동
2nd row상예동
3rd row하예동
4th row애월읍
5th row애월읍

Common Values

ValueCountFrequency (%)
남원읍 14
31.8%
애월읍 5
 
11.4%
대포동 3
 
6.8%
조천읍 3
 
6.8%
서홍동 2
 
4.5%
대정읍 2
 
4.5%
토평동 2
 
4.5%
하원동 2
 
4.5%
표선면 2
 
4.5%
도순동 1
 
2.3%
Other values (8) 8
18.2%

Length

2023-12-13T05:18:32.836089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
남원읍 14
31.8%
애월읍 5
 
11.4%
대포동 3
 
6.8%
조천읍 3
 
6.8%
서홍동 2
 
4.5%
대정읍 2
 
4.5%
토평동 2
 
4.5%
하원동 2
 
4.5%
표선면 2
 
4.5%
동홍동 1
 
2.3%
Other values (8) 8
18.2%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.58048
Minimum126.22779
Maximum127.149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T05:18:32.999847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.22779
5-th percentile126.3015
Q1126.4434
median126.5984
Q3126.69752
95-th percentile126.8388
Maximum127.149
Range0.9212116
Interquartile range (IQR)0.25412153

Descriptive statistics

Standard deviation0.19838041
Coefficient of variation (CV)0.0015672275
Kurtosis1.4066617
Mean126.58048
Median Absolute Deviation (MAD)0.1263531
Skewness0.75724734
Sum5569.5411
Variance0.039354785
MonotonicityNot monotonic
2023-12-13T05:18:33.185888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
126.6580882 3
 
6.8%
126.5884214 2
 
4.5%
127.1490031 2
 
4.5%
126.2656908 1
 
2.3%
126.45619 1
 
2.3%
126.47112 1
 
2.3%
126.6083754 1
 
2.3%
126.6297939 1
 
2.3%
126.3527725 1
 
2.3%
126.4773326 1
 
2.3%
Other values (30) 30
68.2%
ValueCountFrequency (%)
126.2277915 1
2.3%
126.2656908 1
2.3%
126.2937052 1
2.3%
126.3456551 1
2.3%
126.3505941 1
2.3%
126.3527725 1
2.3%
126.3670449 1
2.3%
126.3762457 1
2.3%
126.3950427 1
2.3%
126.3954779 1
2.3%
ValueCountFrequency (%)
127.1490031 2
4.5%
126.853227 1
2.3%
126.7570659 1
2.3%
126.7563774 1
2.3%
126.7555417 1
2.3%
126.7434993 1
2.3%
126.7238262 1
2.3%
126.7162027 1
2.3%
126.7063341 1
2.3%
126.6980129 1
2.3%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct40
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.517795
Minimum33.235973
Maximum37.578277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T05:18:33.386327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.235973
5-th percentile33.24426
Q133.270969
median33.306034
Q333.358043
95-th percentile33.522108
Maximum37.578277
Range4.3423032
Interquartile range (IQR)0.087074475

Descriptive statistics

Standard deviation0.89940611
Coefficient of variation (CV)0.02683369
Kurtosis18.999594
Mean33.517795
Median Absolute Deviation (MAD)0.04066375
Skewness4.469558
Sum1474.783
Variance0.80893135
MonotonicityNot monotonic
2023-12-13T05:18:33.545308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
33.3060336 3
 
6.8%
33.3420367 2
 
4.5%
37.5782765 2
 
4.5%
33.2359733 1
 
2.3%
33.25666 1
 
2.3%
33.27863 1
 
2.3%
33.2602758 1
 
2.3%
33.299995 1
 
2.3%
33.2659768 1
 
2.3%
33.2713948 1
 
2.3%
Other values (30) 30
68.2%
ValueCountFrequency (%)
33.2359733 1
2.3%
33.2368537 1
2.3%
33.2435151 1
2.3%
33.24848 1
2.3%
33.2520233 1
2.3%
33.25666 1
2.3%
33.2577332 1
2.3%
33.2602758 1
2.3%
33.2647629 1
2.3%
33.2659768 1
2.3%
ValueCountFrequency (%)
37.5782765 2
4.5%
33.5247654 1
2.3%
33.5070493 1
2.3%
33.5060423 1
2.3%
33.4612741 1
2.3%
33.4501563 1
2.3%
33.4355869 1
2.3%
33.3988071 1
2.3%
33.3701721 1
2.3%
33.3689588 1
2.3%

경사정도(0 / 1 / 2)
Categorical

Distinct3
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Memory size484.0 B
0
30 
1
11 
2
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 30
68.2%
1 11
 
25.0%
2 3
 
6.8%

Length

2023-12-13T05:18:33.703262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:33.848198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
68.2%
1 11
 
25.0%
2 3
 
6.8%

원지정비(O / X)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
O
42 
X
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowO
2nd rowO
3rd rowO
4th rowO
5th rowO

Common Values

ValueCountFrequency (%)
O 42
95.5%
X 2
 
4.5%

Length

2023-12-13T05:18:33.982333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:34.124030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
o 42
95.5%
x 2
 
4.5%

나무간격(m)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size484.0 B
4.5
19 
4.0
16 
3.5
5 
5.0
3 
4.2
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row4.5
2nd row5.0
3rd row4.5
4th row4.5
5th row4.0

Common Values

ValueCountFrequency (%)
4.5 19
43.2%
4.0 16
36.4%
3.5 5
 
11.4%
5.0 3
 
6.8%
4.2 1
 
2.3%

Length

2023-12-13T05:18:34.246392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:34.370952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4.5 19
43.2%
4.0 16
36.4%
3.5 5
 
11.4%
5.0 3
 
6.8%
4.2 1
 
2.3%

시설(보유 설비)
Categorical

Distinct20
Distinct (%)45.5%
Missing0
Missing (%)0.0%
Memory size484.0 B
점적호스, 개폐기, 우산식, 방풍망
6 
점적호스
4 
개폐기
4 
개폐기, 우산식, 스프링쿨러
4 
점적호스, 개폐기, 우산식, 스프링쿨러, 방풍망
4 
Other values (15)
22 

Length

Max length31
Median length24
Mean length14.568182
Min length3

Unique

Unique11 ?
Unique (%)25.0%

Sample

1st row우산식, 스프링쿨러
2nd row개폐기, 우산식, 스프링쿨러
3rd row점적호스, 개폐기, 우산식, 스프링쿨러, 방풍망
4th row점적호스, 개폐기, 우산식
5th row점적호스, 개폐기, 우산식, 방풍망

Common Values

ValueCountFrequency (%)
점적호스, 개폐기, 우산식, 방풍망 6
13.6%
점적호스 4
 
9.1%
개폐기 4
 
9.1%
개폐기, 우산식, 스프링쿨러 4
 
9.1%
점적호스, 개폐기, 우산식, 스프링쿨러, 방풍망 4
 
9.1%
우산식 3
 
6.8%
점적호스, 개폐기, 우산식 3
 
6.8%
점적호스, 우산식, 스프링쿨러, 방풍망 3
 
6.8%
점적호스, 개폐기, 우산식, 스프링쿨러 2
 
4.5%
개폐기, 우산식 1
 
2.3%
Other values (10) 10
22.7%

Length

2023-12-13T05:18:34.517765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
점적호스 30
23.3%
우산식 30
23.3%
개폐기 29
22.5%
스프링쿨러 18
14.0%
방풍망 17
13.2%
방상팬 1
 
0.8%
방풍망(일부 1
 
0.8%
개폐기(일부 1
 
0.8%
스프링쿨러(일부 1
 
0.8%
우산식(일부 1
 
0.8%

당도(brix)_2020년 11월 1주차
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.010909
Minimum9.62
Maximum12.96
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T05:18:34.678539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.62
5-th percentile10.0895
Q110.3575
median11.01
Q311.4975
95-th percentile12.2855
Maximum12.96
Range3.34
Interquartile range (IQR)1.14

Descriptive statistics

Standard deviation0.71485506
Coefficient of variation (CV)0.064922438
Kurtosis0.059378551
Mean11.010909
Median Absolute Deviation (MAD)0.585
Skewness0.4605188
Sum484.48
Variance0.51101776
MonotonicityNot monotonic
2023-12-13T05:18:34.843346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
10.98 3
 
6.8%
10.25 2
 
4.5%
10.33 2
 
4.5%
11.21 2
 
4.5%
11.63 2
 
4.5%
11.49 1
 
2.3%
10.51 1
 
2.3%
10.28 1
 
2.3%
10.01 1
 
2.3%
11.25 1
 
2.3%
Other values (28) 28
63.6%
ValueCountFrequency (%)
9.62 1
2.3%
10.01 1
2.3%
10.07 1
2.3%
10.2 1
2.3%
10.25 2
4.5%
10.28 1
2.3%
10.31 1
2.3%
10.33 2
4.5%
10.35 1
2.3%
10.36 1
2.3%
ValueCountFrequency (%)
12.96 1
2.3%
12.43 1
2.3%
12.35 1
2.3%
11.92 1
2.3%
11.79 1
2.3%
11.65 1
2.3%
11.63 2
4.5%
11.62 1
2.3%
11.58 1
2.3%
11.52 1
2.3%

당도(brix)_2020년 11월 2주차
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.495455
Minimum10.02
Maximum13.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T05:18:34.997750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.02
5-th percentile10.57
Q110.96
median11.49
Q311.9025
95-th percentile12.475
Maximum13.3
Range3.28
Interquartile range (IQR)0.9425

Descriptive statistics

Standard deviation0.69221402
Coefficient of variation (CV)0.060216325
Kurtosis-0.10074816
Mean11.495455
Median Absolute Deviation (MAD)0.495
Skewness0.25043928
Sum505.8
Variance0.47916025
MonotonicityNot monotonic
2023-12-13T05:18:35.170440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
10.96 3
 
6.8%
11.74 2
 
4.5%
11.83 2
 
4.5%
10.57 2
 
4.5%
10.71 2
 
4.5%
11.3 1
 
2.3%
11.13 1
 
2.3%
12.49 1
 
2.3%
10.81 1
 
2.3%
11.26 1
 
2.3%
Other values (28) 28
63.6%
ValueCountFrequency (%)
10.02 1
 
2.3%
10.53 1
 
2.3%
10.57 2
4.5%
10.62 1
 
2.3%
10.68 1
 
2.3%
10.71 2
4.5%
10.78 1
 
2.3%
10.81 1
 
2.3%
10.96 3
6.8%
11.13 1
 
2.3%
ValueCountFrequency (%)
13.3 1
2.3%
12.8 1
2.3%
12.49 1
2.3%
12.39 1
2.3%
12.37 1
2.3%
12.34 1
2.3%
12.28 1
2.3%
12.26 1
2.3%
12.0 1
2.3%
11.97 1
2.3%

당도(brix)_2020년 11월 3주차
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)87.8%
Missing3
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean11.880244
Minimum10.47
Maximum13.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T05:18:35.304891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.47
5-th percentile10.81
Q111.18
median12
Q312.42
95-th percentile13.02
Maximum13.69
Range3.22
Interquartile range (IQR)1.24

Descriptive statistics

Standard deviation0.75818694
Coefficient of variation (CV)0.063819139
Kurtosis-0.58889069
Mean11.880244
Median Absolute Deviation (MAD)0.64
Skewness0.1670546
Sum487.09
Variance0.57484744
MonotonicityNot monotonic
2023-12-13T05:18:35.446007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
11.93 2
 
4.5%
12.42 2
 
4.5%
12.29 2
 
4.5%
12.0 2
 
4.5%
12.06 2
 
4.5%
12.89 1
 
2.3%
12.7 1
 
2.3%
13.69 1
 
2.3%
11.08 1
 
2.3%
11.02 1
 
2.3%
Other values (26) 26
59.1%
(Missing) 3
 
6.8%
ValueCountFrequency (%)
10.47 1
2.3%
10.77 1
2.3%
10.81 1
2.3%
10.88 1
2.3%
10.92 1
2.3%
11.02 1
2.3%
11.04 1
2.3%
11.06 1
2.3%
11.08 1
2.3%
11.17 1
2.3%
ValueCountFrequency (%)
13.69 1
2.3%
13.16 1
2.3%
13.02 1
2.3%
12.89 1
2.3%
12.83 1
2.3%
12.79 1
2.3%
12.7 1
2.3%
12.58 1
2.3%
12.47 1
2.3%
12.42 2
4.5%

당도(brix)_2020년 11월 4주차
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)100.0%
Missing16
Missing (%)36.4%
Infinite0
Infinite (%)0.0%
Mean12.1675
Minimum10.71
Maximum13.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T05:18:35.584876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.71
5-th percentile11.127
Q111.425
median12.175
Q312.67
95-th percentile13.5065
Maximum13.7
Range2.99
Interquartile range (IQR)1.245

Descriptive statistics

Standard deviation0.81314946
Coefficient of variation (CV)0.066829624
Kurtosis-0.72052389
Mean12.1675
Median Absolute Deviation (MAD)0.64
Skewness0.22253789
Sum340.69
Variance0.66121204
MonotonicityNot monotonic
2023-12-13T05:18:35.726547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
12.24 1
 
2.3%
10.71 1
 
2.3%
11.23 1
 
2.3%
13.5 1
 
2.3%
12.3 1
 
2.3%
12.18 1
 
2.3%
12.15 1
 
2.3%
12.9 1
 
2.3%
11.88 1
 
2.3%
11.44 1
 
2.3%
Other values (18) 18
40.9%
(Missing) 16
36.4%
ValueCountFrequency (%)
10.71 1
2.3%
11.12 1
2.3%
11.14 1
2.3%
11.23 1
2.3%
11.26 1
2.3%
11.31 1
2.3%
11.38 1
2.3%
11.44 1
2.3%
11.57 1
2.3%
11.88 1
2.3%
ValueCountFrequency (%)
13.7 1
2.3%
13.51 1
2.3%
13.5 1
2.3%
13.45 1
2.3%
12.9 1
2.3%
12.84 1
2.3%
12.79 1
2.3%
12.63 1
2.3%
12.62 1
2.3%
12.47 1
2.3%

당도(brix)_2020년 12월 1주차
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct20
Distinct (%)100.0%
Missing24
Missing (%)54.5%
Infinite0
Infinite (%)0.0%
Mean12.284
Minimum11.48
Maximum13.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size528.0 B
2023-12-13T05:18:35.888426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.48
5-th percentile11.6415
Q111.925
median12.13
Q312.5825
95-th percentile12.986
Maximum13.86
Range2.38
Interquartile range (IQR)0.6575

Descriptive statistics

Standard deviation0.56754689
Coefficient of variation (CV)0.046202124
Kurtosis1.6256319
Mean12.284
Median Absolute Deviation (MAD)0.385
Skewness1.0759347
Sum245.68
Variance0.32210947
MonotonicityNot monotonic
2023-12-13T05:18:36.055807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
12.18 1
 
2.3%
11.48 1
 
2.3%
11.88 1
 
2.3%
12.68 1
 
2.3%
12.46 1
 
2.3%
12.02 1
 
2.3%
12.55 1
 
2.3%
12.87 1
 
2.3%
11.98 1
 
2.3%
12.08 1
 
2.3%
Other values (10) 10
22.7%
(Missing) 24
54.5%
ValueCountFrequency (%)
11.48 1
2.3%
11.65 1
2.3%
11.73 1
2.3%
11.75 1
2.3%
11.88 1
2.3%
11.94 1
2.3%
11.95 1
2.3%
11.98 1
2.3%
12.02 1
2.3%
12.08 1
2.3%
ValueCountFrequency (%)
13.86 1
2.3%
12.94 1
2.3%
12.87 1
2.3%
12.83 1
2.3%
12.68 1
2.3%
12.55 1
2.3%
12.52 1
2.3%
12.46 1
2.3%
12.33 1
2.3%
12.18 1
2.3%

Interactions

2023-12-13T05:18:30.287089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:23.881219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:25.125628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:25.943130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:26.707175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:27.557202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:28.474574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:29.414776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:30.400331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:23.983229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:25.241899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:26.031002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:26.793803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:27.678624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:28.578919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:29.517291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:30.531066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:24.089407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:25.328012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:26.118897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:26.896717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:27.788235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:28.706347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:29.612062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:30.648190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:24.197677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:25.400870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:26.202093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:27.000731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:27.895036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:28.823830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:29.732400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:30.751871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:24.331005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:25.512233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:26.297070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:27.123409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:28.024258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:28.960894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:29.842764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:30.850271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:24.788298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:25.625359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:26.405192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:27.234442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:28.151026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:29.079104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:29.970241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:30.950609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:24.912717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:25.723540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:26.512723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:27.362886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:28.252489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:29.187721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:30.073499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:31.056580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:25.030275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:25.847102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:26.619014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:27.451181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:28.362897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:29.284391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:30.189555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:18:36.174065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호지역(시)지역(읍/면/동)경도위도경사정도(0 / 1 / 2)원지정비(O / X)나무간격(m)시설(보유 설비)당도(brix)_2020년 11월 1주차당도(brix)_2020년 11월 2주차당도(brix)_2020년 11월 3주차당도(brix)_2020년 11월 4주차당도(brix)_2020년 12월 1주차
관리번호1.0000.5420.7060.6030.5920.0000.0000.0000.2030.4290.0000.2370.2920.000
지역(시)0.5421.0001.0000.4470.0000.0550.0000.0000.0000.3540.0000.1600.5100.451
지역(읍/면/동)0.7061.0001.0000.9601.0000.0000.0000.0000.0000.5750.7720.8180.7870.134
경도0.6030.4470.9601.0001.0000.0000.0000.0000.3570.5310.5510.6750.0000.267
위도0.5920.0001.0001.0001.0000.0000.0000.0000.6030.0000.0000.459NaNNaN
경사정도(0 / 1 / 2)0.0000.0550.0000.0000.0001.0000.1960.0000.7600.0000.0000.0000.0000.000
원지정비(O / X)0.0000.0000.0000.0000.0000.1961.0000.5610.6900.0000.0000.0000.0000.000
나무간격(m)0.0000.0000.0000.0000.0000.0000.5611.0000.0000.0000.0000.0000.1890.673
시설(보유 설비)0.2030.0000.0000.3570.6030.7600.6900.0001.0000.0000.0000.3170.0000.490
당도(brix)_2020년 11월 1주차0.4290.3540.5750.5310.0000.0000.0000.0000.0001.0000.8130.8180.7020.651
당도(brix)_2020년 11월 2주차0.0000.0000.7720.5510.0000.0000.0000.0000.0000.8131.0000.9380.9330.606
당도(brix)_2020년 11월 3주차0.2370.1600.8180.6750.4590.0000.0000.0000.3170.8180.9381.0000.9190.872
당도(brix)_2020년 11월 4주차0.2920.5100.7870.000NaN0.0000.0000.1890.0000.7020.9330.9191.0000.669
당도(brix)_2020년 12월 1주차0.0000.4510.1340.267NaN0.0000.0000.6730.4900.6510.6060.8720.6691.000
2023-12-13T05:18:36.345809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경사정도(0 / 1 / 2)나무간격(m)지역(읍/면/동)시설(보유 설비)원지정비(O / X)지역(시)
경사정도(0 / 1 / 2)1.0000.0000.0000.4250.3170.085
나무간격(m)0.0001.0000.0000.0000.6530.000
지역(읍/면/동)0.0000.0001.0000.0000.0000.787
시설(보유 설비)0.4250.0000.0001.0000.4110.000
원지정비(O / X)0.3170.6530.0000.4111.0000.000
지역(시)0.0850.0000.7870.0000.0001.000
2023-12-13T05:18:36.497309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리번호경도위도당도(brix)_2020년 11월 1주차당도(brix)_2020년 11월 2주차당도(brix)_2020년 11월 3주차당도(brix)_2020년 11월 4주차당도(brix)_2020년 12월 1주차지역(시)지역(읍/면/동)경사정도(0 / 1 / 2)원지정비(O / X)나무간격(m)시설(보유 설비)
관리번호1.0000.106-0.030-0.154-0.191-0.143-0.0630.0270.3710.3000.0000.0000.0000.000
경도0.1061.0000.419-0.236-0.304-0.442-0.415-0.5620.3060.7100.0000.0000.0000.058
위도-0.0300.4191.000-0.232-0.205-0.182-0.375-0.1170.0000.7870.0000.0000.0000.353
당도(brix)_2020년 11월 1주차-0.154-0.236-0.2321.0000.8420.8570.8220.6980.3180.1670.0000.0000.0000.000
당도(brix)_2020년 11월 2주차-0.191-0.304-0.2050.8421.0000.9180.8800.7240.0000.3630.0000.0000.0000.000
당도(brix)_2020년 11월 3주차-0.143-0.442-0.1820.8570.9181.0000.9130.8740.0800.4260.0000.0000.0000.000
당도(brix)_2020년 11월 4주차-0.063-0.415-0.3750.8220.8800.9131.0000.8410.4280.4700.0000.0000.0000.000
당도(brix)_2020년 12월 1주차0.027-0.562-0.1170.6980.7240.8740.8411.0000.2440.0000.0000.0000.2740.000
지역(시)0.3710.3060.0000.3180.0000.0800.4280.2441.0000.7870.0850.0000.0000.000
지역(읍/면/동)0.3000.7100.7870.1670.3630.4260.4700.0000.7871.0000.0000.0000.0000.000
경사정도(0 / 1 / 2)0.0000.0000.0000.0000.0000.0000.0000.0000.0850.0001.0000.3170.0000.425
원지정비(O / X)0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.3171.0000.6530.411
나무간격(m)0.0000.0000.0000.0000.0000.0000.0000.2740.0000.0000.0000.6531.0000.000
시설(보유 설비)0.0000.0580.3530.0000.0000.0000.0000.0000.0000.0000.4250.4110.0001.000

Missing values

2023-12-13T05:18:31.207625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:18:31.483172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T05:18:31.642621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

관리번호지역(시)지역(읍/면/동)경도위도경사정도(0 / 1 / 2)원지정비(O / X)나무간격(m)시설(보유 설비)당도(brix)_2020년 11월 1주차당도(brix)_2020년 11월 2주차당도(brix)_2020년 11월 3주차당도(brix)_2020년 11월 4주차당도(brix)_2020년 12월 1주차
01서귀포시대포동126.4524333.269691O4.5우산식, 스프링쿨러11.4911.311.8112.2212.08
12서귀포시상예동126.39504333.2520230O5.0개폐기, 우산식, 스프링쿨러11.5812.2612.5812.84<NA>
23서귀포시하예동126.37624633.2368540O4.5점적호스, 개폐기, 우산식, 스프링쿨러, 방풍망11.7912.813.1613.7<NA>
34제주시애월읍126.39547833.4612741O4.5점적호스, 개폐기, 우산식10.9811.8912.47<NA><NA>
45제주시애월읍126.4819733.3701720O4.0점적호스, 개폐기, 우산식, 방풍망10.9811.5211.93<NA><NA>
58제주시조천읍126.6424533.5060420O4.5점적호스, 개폐기, 우산식, 방풍망12.4312.3913.02<NA><NA>
610제주시조천읍126.64268933.5070490O4.5점적호스, 개폐기, 우산식, 스프링쿨러, 방풍망, 방상팬10.3110.6811.4611.38<NA>
711서귀포시남원읍126.67406833.330570O4.0점적호스10.2510.5711.0611.1211.94
813서귀포시남원읍126.69735233.2847891O4.5점적호스, 우산식, 스프링쿨러, 방풍망10.3311.3510.8811.2611.65
914서귀포시표선면126.75706633.3368941O4.0점적호스, 개폐기, 우산식11.2111.7712.2912.79<NA>
관리번호지역(시)지역(읍/면/동)경도위도경사정도(0 / 1 / 2)원지정비(O / X)나무간격(m)시설(보유 설비)당도(brix)_2020년 11월 1주차당도(brix)_2020년 11월 2주차당도(brix)_2020년 11월 3주차당도(brix)_2020년 11월 4주차당도(brix)_2020년 12월 1주차
3440서귀포시토평동127.14900337.5782770O4.5점적호스, 개폐기, 우산식, 방풍망10.5110.5310.77<NA><NA>
3541서귀포시대정읍126.26569133.2359730O5.0점적호스, 방풍망10.4310.7111.7312.1512.55
3642서귀포시상효동126.61259233.2793971O4.0우산식11.6311.9412.42<NA><NA>
3743서귀포시남원읍126.75554233.3212510O4.5점적호스, 개폐기, 우산식, 방풍망10.3310.9611.0812.1812.02
3844서귀포시남원읍126.69801333.3508830O4.5개폐기11.4211.511.9312.312.46
3945서귀포시성산읍126.85322733.3544050O4.5점적호스, 개폐기, 우산식, 방풍망12.9613.313.69<NA><NA>
4046서귀포시대정읍126.29370533.2577330O3.5점적호스, 개폐기, 방풍망11.6212.3712.713.512.68
4147제주시한경면126.22779133.329960O4.5점적호스, 개폐기, 우산식, 스프링쿨러11.2812.2812.89<NA><NA>
4249서귀포시남원읍126.74349933.3251512X4.0점적호스, 개폐기, 우산식(일부)10.3610.7811.0211.2311.88
4350서귀포시남원읍126.75637733.2948440O4.5개폐기, 우산식, 스프링쿨러9.6210.0210.4710.7111.48